{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading data...\n",
      "25000 train sequences\n",
      "25000 test sequences\n",
      "Pad sequences (samples x time)\n",
      "input_train shape: (25000, 500)\n",
      "input_test shape: (25000, 500)\n"
     ]
    }
   ],
   "source": [
    "from keras.datasets import imdb\n",
    "from keras.preprocessing import sequence\n",
    "from keras.layers import Embedding, SimpleRNN\n",
    "from keras.models import Sequential\n",
    "\n",
    "max_features = 10000\n",
    "maxlen = 500\n",
    "batch_size = 32\n",
    "print('Loading data...')\n",
    "(input_train, y_train), (input_test, y_test) = imdb.load_data(num_words=max_features)\n",
    "print(len(input_train), 'train sequences')\n",
    "print(len(input_test), 'test sequences')\n",
    "print('Pad sequences (samples x time)')\n",
    "input_train = sequence.pad_sequences(input_train, maxlen=maxlen)\n",
    "input_test = sequence.pad_sequences(input_test, maxlen=maxlen)\n",
    "print('input_train shape:', input_train.shape)\n",
    "print('input_test shape:', input_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "157/157 [==============================] - 14s 91ms/step - loss: 0.6243 - acc: 0.6427 - val_loss: 0.4666 - val_acc: 0.8002\n",
      "Epoch 2/10\n",
      "157/157 [==============================] - 14s 90ms/step - loss: 0.3914 - acc: 0.8325 - val_loss: 0.4386 - val_acc: 0.7940\n",
      "Epoch 3/10\n",
      "157/157 [==============================] - 14s 88ms/step - loss: 0.2821 - acc: 0.8856 - val_loss: 0.3505 - val_acc: 0.8616\n",
      "Epoch 4/10\n",
      "157/157 [==============================] - 14s 88ms/step - loss: 0.2149 - acc: 0.9144 - val_loss: 0.3842 - val_acc: 0.8520\n",
      "Epoch 5/10\n",
      "157/157 [==============================] - 14s 88ms/step - loss: 0.1498 - acc: 0.9459 - val_loss: 0.4555 - val_acc: 0.8148\n",
      "Epoch 6/10\n",
      "157/157 [==============================] - 14s 89ms/step - loss: 0.1035 - acc: 0.9656 - val_loss: 0.4633 - val_acc: 0.8390\n",
      "Epoch 7/10\n",
      "157/157 [==============================] - 14s 91ms/step - loss: 0.0673 - acc: 0.9780 - val_loss: 0.5503 - val_acc: 0.8512\n",
      "Epoch 8/10\n",
      "157/157 [==============================] - 14s 89ms/step - loss: 0.0384 - acc: 0.9892 - val_loss: 0.5485 - val_acc: 0.8342\n",
      "Epoch 9/10\n",
      "157/157 [==============================] - 14s 88ms/step - loss: 0.0624 - acc: 0.9789 - val_loss: 0.8120 - val_acc: 0.7504\n",
      "Epoch 10/10\n",
      "157/157 [==============================] - 14s 89ms/step - loss: 0.0229 - acc: 0.9938 - val_loss: 0.6099 - val_acc: 0.8358\n"
     ]
    }
   ],
   "source": [
    "from keras.layers import Dense\n",
    "model = Sequential()\n",
    "model.add(Embedding(max_features, 32))\n",
    "model.add(SimpleRNN(32))\n",
    "model.add(Dense(1, activation='sigmoid'))\n",
    "model.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=['acc'])\n",
    "history = model.fit(input_train, y_train,\n",
    " epochs=10,\n",
    " batch_size=128,\n",
    " validation_split=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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JM2bAokVhIq46VVgpuajppbAwM5telNDjnHnmmTzzzDN7FrNYtWoVn3zyCccddxwXX3wxubm5HH300UyYMKHU17dp04YvvvgCgMmTJ9O+fXt++MMf7pliF0If8169etG1a1fOOOMMtm7dyhtvvMHMmTO55ppr6NatG++//z4jRoxgxowZALz00kt0796dzp07M3LkSL799ts915swYQI9evSgc+fOLF++vNzfT9PsSjbatQsmToSOHeHss6t+vmOPDU0v06fDCy9U/XzVqQqfZal15ZWwcGFyz9mtG9x2W9nHDzroIHr37s1zzz3H0KFDyc/P5+c//zlmxuTJkznooIPYtWsXJ554IosWLaJLly6lnmf+/Pnk5+ezcOFCdu7cSY8ePejZsycAP/vZzxg1ahQAN954I/fddx+XXXYZQ4YMYfDgwZx55pnFzrVt2zZGjBjBSy+9RPv27Tn33HO58847ufLKKwFo1qwZCxYs4I477mDq1Knce++9Zf5+mmZXstFjj8GyZfDEExCbFLXKJk2Cp5+GCy4I0+w2aVLxa9KBauglxDe7xDe3PPHEE/To0YPu3buzZMmSYs0jJc2dO5fTTz+dnJwcmjRpwpAhQ/YcW7x4MccddxydO3cmLy+vzOl3i6xYsYK2bdvSvn17AM477zzmzJmz5/jPfvYzAHr27LlnQq+yaJpdyTY7d4bViLp2hTPOSN55GzaEBx/MvKaXtK2hl1eTTqWhQ4cyZswYFixYwNatW+nZsycffvghU6dOZd68eRx44IGMGDGizGlzKzJixAieeuopunbtyoMPPsjs2bOrFG/RFLxVmX537NixnHrqqTz77LP069ePWbNm7Zlm95lnnmHEiBFcddVVnHvuuVWKVSTZHn4YVq6Ef/wjLEWZTMccE+Z6mTIFzjwTfvzj5J4/FVRDL6FRo0Ycf/zxjBw5ck/tfPPmzey3337sv//+rF27lueee67cc/Tv35+nnnqKb775hi1btvD000/vObZlyxYOOeQQduzYsWfKW4DGjRuzpZQRDUceeSSrVq1i5cqVADzyyCP86Ec/qtTvpml2JZts3x6aRnr1gp/+NDXXmDQJOnTInF4vaVtDj9KwYcM4/fTT9zS9FE0326FDB1q1akW/fv3KfX2PHj34xS9+QdeuXfne975Hr1699hy7+eab6dOnD82bN6dPnz57kvjZZ5/NqFGjuP322/fcDAVo0KABDzzwAGeddRY7d+6kV69eXHTRRZX6vYrWOu3SpQs5OTnFptl95ZVXqFWrFkcffTSDBg0iPz+fKVOmULduXRo1aqSFMCTt3H9/mP72rrvCFNqp0KBBaHrp2zfM2jh9emqukyyaPlcqTX8vicq2bXDEEWEdhNdeS11CL3LddfDHP8KsWfCTn6T2WhXR9LkiklXuvhvWrAkDgVKdzCHceD3qqNDrZdOm1F+vshJK6GY20MxWmNlKMxtbyvH/NbOFsZ//M7ONSY9URATYuhV+9zs4/vjqW5SiqOllzZryF8yIWoUJ3cxqA9OAQUBHYJiZdYwv4+5j3L2bu3cD/gz8vbIBRdUEJPtGfyeJyrRpsHZtqJ1Xp969QxfGe+8NTS/pKJEaem9gpbt/4O7bgXxgaDnlhxHWFd1nDRo0YP369UoWac7dWb9+PQ32ZX5SkSTYsgX+8Ac4+WSooG9CShSNSE3XppdEerm0AD6O2y4E+pRW0MwOA9oCL5dxfDQwGqB169bfOd6yZUsKCwtZt25dAmFJlBo0aEDLli2jDkNqmNtvh/XrQ3fCKDRoEOZ6OfbY0Ee9nIHZkUh2t8WzgRnuvqu0g+4+HZgOoZdLyeN169albdu2SQ5JRLLBxo0wdWroc967d3Rx9O4dlqv7/e/DgKOBA6OLpaREmlzWAK3itlvG9pXmbCrZ3CIiUp5bbw1JParaebyippdRo9Kr6SWRhD4PaGdmbc2sHiFpzyxZyMw6AAcC/05uiCJS061fH6YDOeOMMMle1OrXD71ePv00zMyYLipM6O6+E7gUmAUsA55w9yVmNsnMhsQVPRvId93RFJEkmzIFvvoq9AdPF716haaX+++HCmYDqTZpNVJURKSktWvh8MPhtNMgbvqjtPDtt9CzZ2gKWrwYKrm+zT7RSFERyVh/+EMY6l/GujKRKmp6+eyz9Gh6UUIXkbT1ySdw551w7rkQWxIg7eTmhrleHngAnn022liU0EUkbf32t2ERi/Hjo46kfOPHw9FHh14vpawTX22U0EUkLa1eHaarHTkS0n14SlHTy9q1MGZMdHEooYtIWrrlljCT4o03Rh1JYoqaXh58EJ55JpoYlNBFJO28/35ok77wQmjVquLy6WL8eOjUCUaPhi+/rP7rK6GLSNqZNAnq1oXrr486kn0TddOLErqIpJXly+Gvf4VLLoFDDok6mn3XsyeMHQsPPVT9TS9K6CKSVv77v6Fhw9Aenaluuik0vYwaVb1NL0roIpI2Fi+Gxx+Hyy+H5s2jjqbyippePv8crryy+q6bUQk9Lw/atIFatcJjug0DFpGqmTABGjdO72XeEtWzZ7gH8PDD8PTT1XPNjEnoeXnhzvHq1eAeHkePVlIXyRYLFsDf/x6G0B90UNTRJMdNN0HnzqG3TnU0vWRMQh83LiwOG2/r1rBfRDLf+PFw4IHV20SRavXq7W16ueKK1F8vYxL6Rx/t234RyRxvvhl6hFxzDey/f9TRJFePHnDDDfDII6lvesmYhF7KEqTl7heRzDF+fLgJetllUUeSGjfeCF26hGbiDRtSd52MSeiTJ0NOTvF9OTlhv4hkrrlz4YUXQjfFRo2ijiY1ippevvgitU0vCSV0MxtoZivMbKWZjS2jzM/NbKmZLTGzR5MbJgwfHibqOeywML/DYYeF7eHDk30lEaku7uHG4cEHw8UXRx1NanXvHppe/vpXmPmdRTyTo05FBcysNjAN+DFQCMwzs5nuvjSuTDvgeqCfu39pZt9LRbDDhyuBi2STl1+GV1+F22//7jfwbDRuHCxZkro+9hUmdKA3sNLdPwAws3xgKLA0rswoYJq7fwng7p8nO1ARyS7uoW25VavQtlwT1KsHM2ak7vyJNLm0AD6O2y6M7YvXHmhvZq+b2ZtmNrC0E5nZaDMrMLOCdevWVS5iEckKzz0XerfceGMYWSlVl6ybonWAdsAAYBhwj5kdULKQu09391x3z22eyeN6RaRK3EPPlrZt4Ve/ijqa7JFIk8saIH5G4paxffEKgbfcfQfwoZn9HyHBz0tKlCKSVf7xD5g/P8x5Xrdu1NFkj0Rq6POAdmbW1szqAWcDJe/RPkWonWNmzQhNMB8kL0wRyRa7d4faefv28F//FXU02aXCGrq77zSzS4FZQG3gfndfYmaTgAJ3nxk79hMzWwrsAq5x9/WpDFxEMtOMGfDuu2EepjqJtBFIwszdI7lwbm6uFxQURHJtEYnGrl1hnvDateGdd8Kj7Bszm+/uuaUd0+ejiFSbRx8NKxLNmKFkngoZM/RfRDLbjh1hNaJu3eD006OOJjuphi4i1eLhh+H998Ow91qqSqaE3lYRSbnt2+Hmm6F3bxg8OOpospdq6CKScvfdF1YZu/vuMLmepIZq6CKSUtu2hWmu+/WDn/wk6miym2roIlno889hzJi97dW1a4c+30WP8c8TfazMa2rXhpUrYc2aMG2saueppYQukkXcQ+K88kr46is47zzYb7/Q/3vnzoofS+7btm3fX1Pycdcu+OlPYcCAqN+d7KeELpIlVq8Oq8vPmgV9+8I990DHjlFHFT5kpHqoDV0kw+3aFRaIOPpoeP11+POfw7Ju6ZDMITSzqKmleqiGLpLBli6F888P84oPGgR33aWF02sy1dBFMtD27XtHXb73HjzyCDzzjJJ5TacaukiGeeutUCtfsgSGDYM//Sl1a1RKZlENXSRDfP116Ip47LGwaRP8859hsislcymiGrpIBnjhhbCQ8qpV8Otfw+9+B02aRB2VpBvV0EXS2IYNMGJEGGFZv37ovTJtmpK5lC6hhG5mA81shZmtNLOxpRwfYWbrzGxh7OeC5IcqUnO4w9/+BkcdFVb2GTcOFi6EH/4w6sgknVXY5GJmtYFpwI8Ji0HPM7OZ7r60RNHH3f3SFMQoUqOsWQOXXBIWUu7ZE55/Hrp2jToqyQSJ1NB7Ayvd/QN33w7kA0NTG5ZIzbN7N0yfHgYEPf88TJkS+pcrmUuiEknoLYCP47YLY/tKOsPMFpnZDDNrVdqJzGy0mRWYWcG6desqEa5IdnrvPTjhhDB0v2fPsIjyb36jRZRl3yTrpujTQBt37wK8ADxUWiF3n+7uue6e21x9rUTYuRP+8Afo0iW0kd97L7z0EvzgB1FHJpkokYS+BoivcbeM7dvD3de7+7exzXuBnskJTyR7vf12WMFn7Fg45RRYtiwMGNK8J1JZiST0eUA7M2trZvWAs4GZ8QXM7JC4zSHAsuSFKJJdvvkmJPFeveDTT2HGDHjySTjkkIpfK1KeClvo3H2nmV0KzAJqA/e7+xIzmwQUuPtM4HIzGwLsBDYAI1IYs0jGevVVGDUqtJmPHAlTp8KBB0YdlWQL84gmK87NzfWCgoJIri1S3TZtguuuC2tqHn546M1y4olRRyWZyMzmu3tuacc0UlQkxWbODF0R77kHrr469GBRMpdUyLiEXrQklki6W7sWfvELGDoUmjULfcqnToWcnKgjk2yVcQn93nvDPBbHHQdXXQX5+fD++1rmSqLnHtbx/OQTePDBMGz/qafgllugoCDcBBVJpYwbttCjB1xwAcybB3feCf/7v2H/QQdBbm74T1P0c+ih0cYqmWPnTtiyJbR1F/1s3lz687K2N28u/u2xX79QAenQIbrfS2qWjEvoffuGH4AdO2Dx4pDci35+//u9/6kOPTT08y1K8Lm56lGQzdzDkmyffZZYIo5//vXXFZ+/Th3Yf/+9P02aQJs2e5/HH2vRIvQtr5Vx34Elk2VdL5etW8OIu3nz4D//CY/vvbf3+BFH7E3wvXtD9+5q08wGb74JN9wAr7xS+vGcnO8m49Kel3esQQMN+pHoldfLJeNq6BXJySleiwf48kuYP39vLX7uXHjssXCsdu2wWnp8U03nzlC3bjTxy75ZsiRMLfuPf4SVe269NcyFEp+MmzTRnChSM2RdDT1Rn35avKlm3rywmACEhQS6dSveXNO+vb4+p5NVq2DChLA4cuPGcM01cOWV0KhR1JGJpFZ5NfQam9BLcocPP9zbTDNvHixYsLdttUmTUPOLr8m3bq2v4NVt7drQa+Tuu8O3q0svDcPomzaNOjKR6qGEXkm7doUJk4oS/H/+A4sWhZuxEJpq7rknLNorqbVxY+jDfdttYSzC+efD+PHh5qNITaKRopVUuzZ06gS/+hXccUfoS7xlC7z1Ftx+e3jer1/4qp9ILwnZd1u3wh//GIbLT54MgweHnix3361kLlKSEvo+ql8/tK1fdlnoMnnxxfCnP4UbqS+9FHV02WPHjpC027ULc6Acc0xoAsvPD/czROS7lNCroHHjsAL7q6+GXhQnnRRm0tu0KerIMtfu3aEHUseOcNFFoZ/3q6/Cs8+GLqYiUjYl9CTo3x/eeSf0tLj//pCMnn466qgyi3tI2j16wC9/CQ0bhvfwtdfC+ysiFVNCT5KGDUNb75tvhh4XQ4aExKSlUytWlLRPPTXcl/jrX8PgsMGD1YtIZF8ooSdZr17h5unEiWElmo4dQ7uvJg/7rnfeCUn7uONg5cpw43nZMhg+XH3+RSojof82ZjbQzFaY2UozG1tOuTPMzM2s1C41NUW9emHQy4IF0LYtDBsGp50WZuGTkLx/+cvQJv7662H+nfffDzeY69WLOjqRzFVhQjez2sA0YBDQERhmZh1LKdcYuAJ4K9lBZqpOneCNN2DKFHj++VBbv+++mltb/+STkLSPOioM1R87Fj74IPRi0Xw6IlWXSA29N7DS3T9w9+1APjC0lHI3A38AtiUxvoxXpw785jdhQFLXrmHq35/8JIxKrSk2bAhJ+4gjwnSyF14YauS//a1mvxRJpkQSegvg47jtwti+PcysB9DK3Z8p70RmNtrMCsysYF0G3y3Mywvd6WrVCo95eRW/pl27MBPgnXeGG6edOoXBSbt3pzra6Hz9dUjahx8evqWccQasWAF/+QscfHDU0YlknyrfejKzWsCtwNUVlXX36e6e6+65zZs3r+qlI5GXB6NHw+rVoelk9eqwnUhSr1Ur9K1esiT06rjiinBDcPny1MddnbZvD0n7Bz8IMyEWdet85JGQ3EUkNRJJ6GuAVnHbLWP7ijQGOgGzzWwVcAwwM1tvjI4bF4ajx9u6NexPVOvWoc/1ww+HXh3dusHvfrd3jphMtWtXSNodOoSRtB06hJueM2eGkbQiklqJJPR5QDsza2tm9YCzgZlFB919k7s3c/c27t4GeBMY4u7pPfNWJX300b7tL4sZnHNOSOg//WlYnKFPn9D/OtNs3AhPPBE+mM49Fw44AP71r9DEFD8vvYikVoUJ3d13ApcCs4BlwBPuvsTMJpnZkFQHmG5at963/RX5/vfhb3+DJ58MvUB69YIbb4Rvv618jKm2cWMYxXn11WFK4YMOCqvbb98Ojz8e+uGffLIGBYlUN02fu4+K2tDjm11ycmD69DAgpio2bICrroKHHgpd++67Lz2m5v3yy7DK0+zZ4WfhwnD/oH79EN+PfhR+jjtOKwOJpJrmQ0+yvLzQZv7RR6FmPnly1ZN5vH/9K3xoFBaGG6e33AL77Ze881dkw4biCfydd/Ym8L59Q/IeMCA0ETVoUH1xiYgSekbasiUMvLnjjtAz5J574IQTUnOtDRtgzpy9CXzRopDAGzQonsB791YCF4maEnoGmzMnrM6zcmWYmnfKlLDwcVWsX//dBA4hWffrVzyB169fxV9ARJKqvISuFs80179/SLgTJsD//E/o7njXXWFSq0R98UXxBP7uu2F/w4Yhgd98c0jgvXopgYtkMtXQM8i8eTByZFgp6Ze/DCslNWv23XLr1hVP4IsXh/05OcVr4L16aTIskUyjGnqW6NUL5s8Pg5AmT4YXXoA//xmOP754Al+yJJTPyYEf/jDM9jhgAOTmKoGLZDPV0DPU4sWhtj5v3t59++0XEnhRDTw3F+rWjSxEEUkB1dCzUNHUvPffH3qpDBgQBvkogYvUXEroGaxOndBfXUQEtASdiEjWUEIXEckSSugiIllCCV1EJEsooYuIZAkldBGRLKGELiKSJRJK6GY20MxWmNlKMxtbyvGLzOxdM1toZq+ZWcfkhyoiIuWpMKGbWW1gGjAI6AgMKyVhP+rund29G/BH4NZkByoiIuVLpIbeG1jp7h+4+3YgHxgaX8DdN8dt7gdEM0GMiEgNlsjQ/xbAx3HbhUCfkoXM7BLgKqAeUOraOmY2GhgN0LqyqyqLiEipknZT1N2nufsPgOuAG8soM93dc909t3nz5sm6tIiIkFhCXwO0ittuGdtXlnzgtCrEJAnKy4M2baBWrfCYlxd1RCISpUQS+jygnZm1NbN6wNnAzPgCZtYubvNU4L3khSilycsLMy2uXh0WdF69OmwrqYvUXBUmdHffCVwKzAKWAU+4+xIzm2RmQ2LFLjWzJWa2kNCOfl6qApZg3DjYurX4vq1bw34RqZm0YlGGqlUr1MxLMoPdu6s/HhGpHuWtWKSRohmqrE5C6jwkUnMpoWeoyZPDItDxcnLCfhGpmZTQM9Tw4TB9Ohx2WGhmOeywsD18eNSRiUhUtKZoBhs+XAlcRPZSDV1EJEsooYuIZAkldBGRLKGELiKSJZTQRUSyhBK6iEiWUEIXEckSSugiIllCCV1EJEsooUuVaaENkfSgof9SJUULbRTNzV600AZoWgKR6qYaulSJFtoQSR8JJXQzG2hmK8xspZmNLeX4VWa21MwWmdlLZnZY8kOVdPTRR/u2X0RSp8KEbma1gWnAIKAjMMzMOpYo9jaQ6+5dgBnAH5MdqKQnLbQhkj4SqaH3Bla6+wfuvh3IB4bGF3D3V9y96Iv3m0DL5IYp6UoLbYikj0QSegvg47jtwti+spwPPFeVoCRzaKENkfSR1F4uZvZfQC7wozKOjwZGA7TWd/KsoYU2RNJDIjX0NUCruO2WsX3FmNlJwDhgiLt/W9qJ3H26u+e6e27z5s0rE6+IiJQhkYQ+D2hnZm3NrB5wNjAzvoCZdQfuJiTzz5MfpoiIVKTChO7uO4FLgVnAMuAJd19iZpPMbEis2BSgEfA3M1toZjPLOJ2IiKRIQm3o7v4s8GyJfePjnp+U5LhERGQfaaSoiEiWUEIXEckSSugiIllCCV1EJEsooUvW0LzsUtNpPnTJCpqXXUQ1dMkSmpddRAldsoTmZRdRQpcsoXnZRZTQJUtoXnYRJXTJEpqXXUS9XCSLaF52qelUQxcRyRJK6CIiWUIJXUQkSyihi4hkiYQSupkNNLMVZrbSzMaWcry/mS0ws51mdmbywxTJDJpPRqJUYUI3s9rANGAQ0BEYZmYdSxT7CBgBPJrsAEUyRdF8MqtXg/ve+WSU1KW6JFJD7w2sdPcP3H07kA8MjS/g7qvcfRGwOwUximQEzScjUUskobcAPo7bLoztE5E4mk9GolatN0XNbLSZFZhZwbp166rz0iIpp/lkJGqJJPQ1QKu47ZaxffvM3ae7e6675zZv3rwypxBJW5pPRqKWSEKfB7Qzs7ZmVg84G5iZ2rBEMo/mk5GombtXXMjsFOA2oDZwv7tPNrNJQIG7zzSzXsD/Aw4EtgGfufvR5Z0zNzfXCwoKqhq/iEiNYmbz3T23tGMJTc7l7s8Cz5bYNz7u+TxCU4yIiEREI0VFspAGONVMSugiWUYDnL6rpnzAKaGLZBkNcCquJn3AKaGLZBkNcCquJn3AKaGLZBkNcCquJn3AKaGLZBkNcCquJn3AKaGLZBkNcCquJn3AKaGLZKHhw2HVKti9OzxGlczToXdJTfqAS2hgkYjIvirqXVJ0Q7KodwlUfzIdPjw7E3hJqqGLSErUpN4l6UIJXURSoib1LkkXSugikhI1qXdJolJ9T0EJXURSoib1LklEdYxYVUIXkZSoSb1LElEd9xQSmg89FTQfuojUJLVqhZp5SWahe2miypsPXTV0EZFqUB33FBJK6GY20MxWmNlKMxtbyvH6ZvZ47PhbZtYmeSGKiGS+6rinUGFCN7PawDRgENARGGZmHUsUOx/40t2PAP4X+EPyQhQRyXzVcU8hkZGivYGV7v4BgJnlA0OBpXFlhgITY89nAH8xM/OoGuhFRNJQqkesJtLk0gL4OG67MLav1DLuvhPYBDRNRoAiIpKYar0pamajzazAzArWrVtXnZcWEcl6iST0NUCruO2WsX2lljGzOsD+wPqSJ3L36e6e6+65zZs3r1zEIiJSqkQS+jygnZm1NbN6wNnAzBJlZgLnxZ6fCbys9nMRkepV4U1Rd99pZpcCs4DawP3uvsTMJgEF7j4TuA94xMxWAhsISV9ERKpRZCNFzWwdsDqSiydPM+CLqINII3o/9tJ7UZzej+Kq8n4c5u6ltllHltCzgZkVlDUEtybS+7GX3ovi9H4Ul6r3Q0P/RUSyhBK6iEiWUEKvmulRB5Bm9H7spfeiOL0fxaXk/VAbuohIllANXUQkSyihi4hkCSX0SjCzVmb2ipktNbMlZnZF1DFFzcxqm9nbZvbPqGOJmpkdYGYzzGy5mS0zs2OjjilKZjYm9v9ksZk9ZmYNoo6pupjZ/Wb2uZktjtt3kJm9YGbvxR4PTNb1lNArZydwtbt3BI4BLilljvia5gpgWdRBpIk/Af9y9w5AV2rw+2JmLYDLgVx370QYbV6TRpI/CAwssW8s8JK7twNeim0nhRJ6Jbj7p+6+IPZ8C+E/bMkphWsMM2sJnArcG3UsUTOz/YH+hOkwcPft7r4x0qCiVwdoGJu4Lwf4JOJ4qo27zyFMhxJvKPBQ7PlDwGnJup4SehXFltvrDrwVcShRug24FtiHpW6zVltgHfBArAnqXjPbL+qgouLua4CpwEfAp8Amd38+2qgi9313/zT2/DPg+8k6sRJ6FZhZI+BJ4Ep33xx1PFEws8HA5+4+P+pY0kQdoAdwp7t3B74miV+pM02sfXgo4YPuUGA/M/uvaKNKH7FZaZPWd1wJvZLMrC4hmee5+9+jjidC/YAhZrYKyAdOMLO/RhtSpAqBQncv+sY2g5Dga6qTgA/dfZ277wD+DvSNOKaorTWzQwBij58n68RK6JVgZkZoI13m7rdGHU+U3P16d2/p7m0IN7tedvcaWwNz98+Aj83syNiuEym+/m5N8xFwjJnlxP7fnEgNvkkcE79+xHnAP5J1YiX0yukHnEOojS6M/ZwSdVCSNi4D8sxsEdAN+G204UQn9k1lBrAAeJeQc2rMNABm9hjwb+BIMys0s/OB3wM/NrP3CN9gfp+062nov4hIdlANXUQkSyihi4hkCSV0EZEsoYQuIpIllNBFRLKEErqISJZQQhcRyRL/HxrIDXR4htCkAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "acc = history.history['acc']\n",
    "val_acc = history.history['val_acc']\n",
    "loss = history.history['loss']\n",
    "val_loss = history.history['val_loss']\n",
    "epochs = range(1, len(acc) + 1)\n",
    "plt.plot(epochs, acc, 'bo', label='Training acc')\n",
    "plt.plot(epochs, val_acc, 'b', label='Validation acc')\n",
    "plt.title('Training and validation accuracy')\n",
    "plt.legend()\n",
    "plt.figure()\n",
    "plt.plot(epochs, loss, 'bo', label='Training loss')\n",
    "plt.plot(epochs, val_loss, 'b', label='Validation loss')\n",
    "plt.title('Training and validation loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
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